Page 53 - Control Theory in Biomedical Engineering
P. 53
40 Control theory in biomedical engineering
Ottesen, J., Olufsen, M., Larsen, J., 2004. Applied Mathematical Models in Human Physi-
ology. Society for Industrial and Applied Mathematics.
Oxtoby, N.P., et al., 2018. Data-driven models of dominantly-inherited Alzheimer’s disease
progression. Brain 141 (5), 1529–1544.
Palumbo, P., et al., 2013. Mathematical modeling of the glucose–insulin system: a review.
Math. Biosci. 244 (2), 69–81.
Panahi, S., et al., 2017. Modeling of epilepsy based on chaotic artificial neural network.
Chaos, Solitons Fractals 105, 150–156.
Panahi, S., et al., 2019. A new chaotic network model for epilepsy. Appl. Math. Comput.
346, 395–407.
Paoletti, N., et al., 2019. Data-driven robust control for a closed-loop artificial pancreas.
In: IEEE/ACM Transactions on Computational Biology and Bioinformatics. https://
doi.org/10.1109/tcbb.2019.2912609.
Patcharatrakul, T., et al., 2020. Biofeedback therapy. In: Clinical and Basic Neurogastroen-
terology and Motility. Elsevier, pp. 517–532.
Perelson, A., 2002. Modeling viral and immune system dynamics. Nat. Rev. Immunol.
2 (1), 28.
Pia Saccomani, M., Audoly, S., D’Angio `, L., 2003. Parameter identifiability of nonlinear sys-
tems: the role of initial conditions. Automatica 39 (4), 619–632.
Pironet, A., et al., 2019. Practical identifiability analysis of a minimal cardiovascular system
model. Comput. Methods Prog. Biomed. 171, 53–65.
Quarteroni, A., 2001. Modeling the cardiovascular system—a mathematical adventure: Part
I. SIAM News 34 (5), 1–3.
Quarteroni, A., Formaggia, L., Veneziani, A., 2009. Cardiovascular Mathematics: Modeling
and Simulation of the Circulatory System, Modeling, Simulation and Applications.
Springer Science & Business Media.
Quarteroni, A., Manzoni, A., Vergara, C., 2017. The cardiovascular system: Mathematical
modeling, numerical algorithms and clinical applications. Acta Numerica 26, 365–590.
Rajagopal, K., et al., 2019. Chaotic dynamics of a fractional order glucose-insulin regulatory
system. Front. Inform. Technol. Electron. Eng., 1–11.
Ramsay, D.S., Woods, S.C., 2014. Clarifying the roles of homeostasis and allostasis in phys-
iological regulation. Psychol. Rev. 121 (2), 225–247.
Raue, A., et al., 2009. Structural and practical identifiability analysis of partially observed
dynamical models by exploiting the profile likelihood. Bioinformatics 25 (15),
1923–1929.
Reisman, S., et al., 2018. Biomedical Engineering Principles. CRC Press.
Ribari c, S., Kordas ˇ, M., 2011. Teaching cardiovascular physiology with equivalent elec-
tronic circuits in a practically oriented teaching module. Adv. Physiol. Educ. 35 (2),
149–160.
Rideout, V., Beneken, J., 1975. Parameter estimation applied to physiological systems. Math.
Comput. Simul. 17 (1), 23–36.
Rihan, F.A., Lakshmanan, S., Maurer, H., 2019. Optimal control of tumour-immune model
with time-delay and immuno-chemotherapy. Appl. Math. Comput. 353, 147–165.
Roberts, F., 1976. Discrete Mathematical Models, With Applications to Social, Biological,
and Environmental Problems. Prentice-Hall, Englewood Cliffs, NJ.
Rossler, O.E., Rossler, R., 1994. Chaos in physiology. Integr. Physiol. Behav. Sci. 29 (3),
328–333.
Rostami, Z., Mousavi, M., Rajagopal, K., Boubaker, O., Jafari, S., 2019. Chaotic solutions
in a forced two-dimensional Hindmarsh-Rose neuron. In: Recent Advances in Chaotic
Systems and Synchronization. Academic Press, pp. 187–209. 10.1016/B978-0-12-
815838-8.00010-8.